{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io as scio\n",
    "import cv2\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import time\n",
    "from sklearn.metrics import cohen_kappa_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def load_data(flag='indian'):\n",
    "    if flag == 'indian':\n",
    "        Ind_pines_dict = scio.loadmat('N:/RS/DRN_RCN/Indian_pines.mat')\n",
    "        Ind_pines_gt_dict = scio.loadmat('N:/RS/DRN_RCN/Indian_pines_gt.mat')\n",
    "\n",
    "        print(Ind_pines_dict['indian_pines'].shape)\n",
    "        print(Ind_pines_gt_dict['indian_pines_gt'].shape)\n",
    "\n",
    "        # remove the water absorption bands\n",
    "\n",
    "        no_absorption = list(set(np.arange(0, 103)) | set(np.arange(108, 149)) | set(np.arange(163, 219)))\n",
    "\n",
    "        original = Ind_pines_dict['indian_pines'][:, :, no_absorption].reshape(145 * 145, 200)\n",
    "\n",
    "        print(original.shape)\n",
    "        print('Remove wate absorption bands successfully!')\n",
    "\n",
    "        gt = Ind_pines_gt_dict['indian_pines_gt'].reshape(145 * 145, 1)\n",
    "\n",
    "        r = Ind_pines_dict['indian_pines'].shape[0]\n",
    "        c = Ind_pines_dict['indian_pines'].shape[1]\n",
    "        categories = 17\n",
    "    if flag == 'pavia':\n",
    "        pav_univ_dict = scio.loadmat('N:/RS/DRN_RCN/PaviaU.mat')\n",
    "        pav_univ_gt_dict = scio.loadmat('N:/RS/DRN_RCN/PaviaU_gt.mat')\n",
    "\n",
    "        print(pav_univ_dict['paviaU'].shape)\n",
    "        print(pav_univ_gt_dict['paviaU_gt'].shape)\n",
    "\n",
    "        original = pav_univ_dict['paviaU'].reshape(610 * 340, 103)\n",
    "        gt = pav_univ_gt_dict['paviaU_gt'].reshape(610 * 340, 1)\n",
    "\n",
    "        r = pav_univ_dict['paviaU'].shape[0]\n",
    "        c = pav_univ_dict['paviaU'].shape[1]\n",
    "        categories = 10\n",
    "\n",
    "    rows = np.arange(gt.shape[0])  # 从0开始\n",
    "    # 行号(ID)，特征数据，类别号\n",
    "    All_data = np.c_[rows, original, gt]\n",
    "\n",
    "    # 剔除非0类别，获取所有labeled数据\n",
    "    labeled_data = All_data[All_data[:, -1] != 0, :]\n",
    "    rows_num = labeled_data[:, 0]  # 所有labeled数据的ID\n",
    "\n",
    "    return All_data, labeled_data, rows_num, categories, r, c, flag\n",
    "\n",
    "def generation_num(flag,labeled_data, rows_num, All_data):\n",
    "\n",
    "    train_num = []\n",
    "\n",
    "    for i in np.unique(labeled_data[:, -1]):\n",
    "        temp = labeled_data[labeled_data[:, -1] == i, :]\n",
    "        temp_num = temp[:, 0]  # all ID of a special class\n",
    "        #print(i, temp_num.shape[0])\n",
    "        np.random.shuffle(temp_num)  # random sequence\n",
    "        if flag == 'indian':\n",
    "            if i == 1:\n",
    "                train_num.append(temp_num[0:33])\n",
    "            elif i == 7:\n",
    "                train_num.append(temp_num[0:20])\n",
    "            elif i == 9:\n",
    "                train_num.append(temp_num[0:14])\n",
    "            elif i == 16:\n",
    "                train_num.append(temp_num[0:75])\n",
    "            else:\n",
    "                train_num.append(temp_num[0:100])\n",
    "        if flag == 'pavia':\n",
    "            train_num.append(temp_num[0:100])\n",
    "\n",
    "    trn_num = [x for j in train_num for x in j]  # merge\n",
    "    tes_num = list(set(rows_num) - set(trn_num))\n",
    "    pre_num = list(set(range(0, All_data.shape[0])) - set(trn_num))\n",
    "    print('number of training sample', len(trn_num))\n",
    "    return rows_num, trn_num, tes_num, pre_num\n",
    "\n",
    "def normlization(data_spat, mi, ma,flag='trn'):\n",
    "\n",
    "    scaler = MinMaxScaler(feature_range=(mi, ma))\n",
    "\n",
    "    spat_data = data_spat.reshape(-1, data_spat.shape[-1])\n",
    "    data_spat_new = scaler.fit_transform(spat_data).reshape(data_spat.shape)\n",
    "\n",
    "    print('{}_spat:{}'.format(flag,data_spat_new.shape))\n",
    "    print('{} Spatial dataset normalization Finished!'.format(flag))\n",
    "    return data_spat_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(145, 145, 220)\n",
      "(145, 145)\n",
      "(21025, 200)\n",
      "Remove wate absorption bands successfully!\n",
      "Data has been loaded successfully!\n"
     ]
    }
   ],
   "source": [
    "All_data,labeled_data,rows_num,categories,r,c,FLAG=load_data(flag='indian')\n",
    "print('Data has been loaded successfully!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21025, 2)\n",
      "PCA Finished!\n"
     ]
    }
   ],
   "source": [
    "X = All_data[:,1:-1]\n",
    "mu = np.mean(X,axis=0)\n",
    "X_norm = X-mu\n",
    "    \n",
    "sigma = np.cov(X_norm.T)\n",
    "[U, S, V] = np.linalg.svd(sigma)\n",
    "num_PC=0\n",
    "tmp=0\n",
    "for i in range(S.shape[0]):\n",
    "    tmp+=S[i]\n",
    "    num_PC+=1\n",
    "    if tmp/np.sum(S)>0.9:#取总信息量大于90%的成分\n",
    "        break\n",
    "X_P = np.dot(X_norm,U[:,:num_PC])\n",
    "print(X_P.shape)\n",
    "print('PCA Finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[6,6])\n",
    "for i in range(num_PC):\n",
    "    plt.subplot(1,num_PC,i+1)\n",
    "    plt.imshow(X_P[:,i].reshape(-1,c),cmap='gray')\n",
    "    plt.title('PC'+str(i+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21025, 24)\n",
      "EMP generation finished!\n"
     ]
    }
   ],
   "source": [
    "#https://blog.csdn.net/Augurlee/article/details/97025247\n",
    "def imreconstruct_OPENING(marker, mask, SE):\n",
    "    \"\"\"\n",
    "    描述：以mask为约束，连续膨胀marker，实现形态学重建，其中mask >= marker\n",
    "    \n",
    "    参数：\n",
    "        - marker 标记图像，单通道/三通道图像\n",
    "        - mask   模板图像，与marker同型\n",
    "        - conn   联通性重建结构元，参照matlab::imreconstruct::conn参数，默认为8联通\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        marker_pre = marker\n",
    "        dilation = cv2.dilate(marker, kernel=SE)\n",
    "        marker = np.minimum(dilation, mask)\n",
    "        if (marker_pre == marker).all():\n",
    "            break\n",
    "    return marker\n",
    "\n",
    "def imreconstruct_CLOSING(marker, mask, SE):\n",
    "    \"\"\"\n",
    "    描述：以mask为约束，连续腐蚀marker，实现形态学重建，其中mask<= marker\n",
    "    \n",
    "    参数：\n",
    "        - marker 标记图像，单通道/三通道图像\n",
    "        - mask   模板图像，与marker同型\n",
    "        - conn   联通性重建结构元，参照matlab::imreconstruct::conn参数，默认为8联通\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        marker_pre = marker\n",
    "        erodtion = cv2.erode(marker, kernel=SE)\n",
    "        marker = np.maximum(erodtion, mask)\n",
    "        if (marker_pre == marker).all():\n",
    "            break\n",
    "    return marker\n",
    "\n",
    "scale=6 #自定义结构元素的尺度数\n",
    "X_OE=np.zeros([All_data.shape[0],scale*num_PC])#opening & closing\n",
    "X_CE=np.zeros([All_data.shape[0],scale*num_PC])#opening & closing\n",
    "for i in range(num_PC):\n",
    "    mask=X_P[:,i].reshape(-1,c)\n",
    "    for j in range(scale):\n",
    "        SE=cv2.getStructuringElement(cv2.MORPH_RECT,(2*j+1,2*j+1))\n",
    "        RO_pre=cv2.erode(mask,kernel=SE)#开重建，先腐蚀\n",
    "        RC_pre=cv2.dilate(mask,kernel=SE)#闭重建，先膨胀\n",
    "        RO_res=imreconstruct_OPENING(RO_pre, mask, np.ones([3,3]))\n",
    "        RC_res=imreconstruct_CLOSING(RC_pre, mask, np.ones([3,3]))\n",
    "        X_OE[:,i*scale+j]=RO_res.reshape(-1)\n",
    "        X_CE[:,i*scale+j]=RC_res.reshape(-1)\n",
    "        \n",
    "X_E=np.concatenate([X_OE,X_CE],axis=1)\n",
    "print(X_E.shape)\n",
    "print('EMP generation finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[12,12])\n",
    "for i in range(2):\n",
    "    for j in range(num_PC):\n",
    "        for k in range(scale):\n",
    "            plt.subplot(4,scale,(i*num_PC*scale)+(j*scale)+k+1)\n",
    "            plt.imshow(X_E[:,(i*num_PC*scale)+(j*scale)+k].reshape(-1,c),\n",
    "                       cmap='gray')\n",
    "            plt.xticks([])\n",
    "            plt.yticks([])\n",
    "            if i==0:\n",
    "                plt.title('OPEN: PC-{} SCALE-{}'.format(j+1,k+1))\n",
    "            else:\n",
    "                plt.title('CLOSE: PC-{} SCALE-{}'.format(j+1,k+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16e3b429cc0>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=[12,12])\n",
    "plt.imshow(X_E[:,5].reshape(-1,c),cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init():\n",
    "    hidden_num=int(np.sqrt(2*num_PC*scale*(categories-1)))\n",
    "    W_ih=np.random.randn(2*num_PC*scale, hidden_num)\n",
    "    b_ih=np.zeros([hidden_num,1])\n",
    "    W_ho=np.random.randn(hidden_num, categories-1)\n",
    "    b_ho=np.zeros([categories-1,1])\n",
    "    para={'W_ih':W_ih,'b_ih':b_ih,'W_ho':W_ho,'b_ho':b_ho}\n",
    "    return para\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "def forward_propagation(X,para):\n",
    "    A=np.dot(X,para['W_ih'])+para['b_ih'].T\n",
    "    h=sigmoid(A)\n",
    "    o=np.dot(h,para['W_ho'])+para['b_ho'].T\n",
    "    score=np.exp(o)\n",
    "    return score,o,h,para,X\n",
    "\n",
    "def compute_loss(score,y):#log_softmax\n",
    "    N=score.shape[0]\n",
    "    loss=np.sum(-np.log(score[np.arange(N),y]/np.sum(score,axis=1)))\n",
    "    loss=loss/N\n",
    "    return loss\n",
    "\n",
    "def back_propagation(score,y,o,h,para,X):\n",
    "    grad = {}\n",
    "    N=score.shape[0]\n",
    "    #softmax反向传播时要分情况\n",
    "    dscore=-score/(np.sum(score,axis=1)).reshape(N,-1)\n",
    "    dscore[np.arange(N),y]+=1\n",
    "    dscore=dscore*(-1)/N\n",
    "    grad['b_ho']=np.transpose(np.dot(np.ones([1,N]),dscore))\n",
    "    grad['W_ho']=np.dot(h.T,dscore)\n",
    "    dh=np.dot(dscore,para['W_ho'].T)\n",
    "    dA=dh*(1-dh)\n",
    "    grad['b_ih']=np.transpose(np.dot(np.ones([1,N]),dA))\n",
    "    grad['W_ih']=np.dot(X.T,dA)\n",
    "    return grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### training & testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training sample 900\n",
      "epoch:  1 loss:  3.631210392769356 acc 0.0\n",
      "epoch:  2 loss:  3.174095427383166 acc 0.01\n",
      "epoch:  3 loss:  2.8770219669001573 acc 0.042222222222222223\n",
      "epoch:  4 loss:  2.68078590448555 acc 0.06111111111111111\n",
      "epoch:  5 loss:  2.547057144609039 acc 0.10333333333333333\n",
      "epoch:  6 loss:  2.4486240178639167 acc 0.11333333333333333\n",
      "epoch:  7 loss:  2.3715031613042825 acc 0.11222222222222222\n",
      "epoch:  8 loss:  2.308881871736812 acc 0.11555555555555555\n",
      "epoch:  9 loss:  2.2569435744856534 acc 0.13777777777777778\n",
      "epoch:  10 loss:  2.213216901940498 acc 0.16444444444444445\n",
      "epoch:  11 loss:  2.1759548010144503 acc 0.19\n",
      "epoch:  12 loss:  2.1438622635159486 acc 0.21555555555555556\n",
      "epoch:  13 loss:  2.1159525877575507 acc 0.23777777777777778\n",
      "epoch:  14 loss:  2.091460362282819 acc 0.25222222222222224\n",
      "epoch:  15 loss:  2.069784069210554 acc 0.26666666666666666\n",
      "epoch:  16 loss:  2.0504461404595493 acc 0.29555555555555557\n",
      "epoch:  17 loss:  2.0330641647978607 acc 0.36777777777777776\n",
      "epoch:  18 loss:  2.017329623930171 acc 0.40444444444444444\n",
      "epoch:  19 loss:  2.002991913111658 acc 0.41888888888888887\n",
      "epoch:  20 loss:  1.989846174904352 acc 0.4255555555555556\n",
      "epoch:  21 loss:  1.9777239400540978 acc 0.43\n",
      "epoch:  22 loss:  1.9664858658539444 acc 0.4411111111111111\n",
      "epoch:  23 loss:  1.9560160598314789 acc 0.44555555555555554\n",
      "epoch:  24 loss:  1.9462176128314272 acc 0.45\n",
      "epoch:  25 loss:  1.937009062041621 acc 0.4533333333333333\n",
      "epoch:  26 loss:  1.92832157416225 acc 0.45222222222222225\n",
      "epoch:  27 loss:  1.9200966899228153 acc 0.4577777777777778\n",
      "epoch:  28 loss:  1.9122845089005496 acc 0.46444444444444444\n",
      "epoch:  29 loss:  1.9048422217658996 acc 0.46444444444444444\n",
      "epoch:  30 loss:  1.8977329182490592 acc 0.46111111111111114\n",
      "epoch:  31 loss:  1.8909246151231744 acc 0.4588888888888889\n",
      "epoch:  32 loss:  1.8843894606638347 acc 0.45444444444444443\n",
      "epoch:  33 loss:  1.8781030813424324 acc 0.44222222222222224\n",
      "epoch:  34 loss:  1.872044043658572 acc 0.4411111111111111\n",
      "epoch:  35 loss:  1.8661934095439443 acc 0.43555555555555553\n",
      "epoch:  36 loss:  1.8605343680711364 acc 0.43333333333333335\n",
      "epoch:  37 loss:  1.8550519295697996 acc 0.43222222222222223\n",
      "epoch:  38 loss:  1.8497326709095316 acc 0.4311111111111111\n",
      "epoch:  39 loss:  1.8445645228186616 acc 0.42777777777777776\n",
      "epoch:  40 loss:  1.8395365917949913 acc 0.4266666666666667\n",
      "epoch:  41 loss:  1.8346390105219474 acc 0.42444444444444446\n",
      "epoch:  42 loss:  1.8298628118023548 acc 0.42333333333333334\n",
      "epoch:  43 loss:  1.8251998219160774 acc 0.42\n",
      "epoch:  44 loss:  1.8206425700383093 acc 0.42\n",
      "epoch:  45 loss:  1.8161842109542792 acc 0.41888888888888887\n",
      "epoch:  46 loss:  1.81181845879835 acc 0.41555555555555557\n",
      "epoch:  47 loss:  1.8075395299505879 acc 0.41444444444444445\n",
      "epoch:  48 loss:  1.8033420935574223 acc 0.41333333333333333\n",
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      "epoch:  50 loss:  1.7951723852006851 acc 0.4122222222222222\n",
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      "epoch:  55 loss:  1.7758714056915696 acc 0.4088888888888889\n",
      "epoch:  56 loss:  1.7721762205734366 acc 0.4088888888888889\n",
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      "epoch:  59 loss:  1.7613700095646503 acc 0.4077777777777778\n",
      "epoch:  60 loss:  1.757853228244429 acc 0.41\n",
      "epoch:  61 loss:  1.7543755687282006 acc 0.41\n",
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      "epoch:  66 loss:  1.7375116319392756 acc 0.4122222222222222\n",
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      "epoch:  89 loss:  1.6675811830799494 acc 0.4211111111111111\n",
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      "epoch:  166 loss:  1.470677033798797 acc 0.51\n"
     ]
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    {
     "name": "stdout",
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     "text": [
      "epoch:  167 loss:  1.4683292567994732 acc 0.5144444444444445\n",
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    {
     "name": "stdout",
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     "name": "stdout",
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      "epoch:  1961 loss:  1.2708339564095903 acc 0.4722222222222222\n",
      "epoch:  1962 loss:  1.2709037721129244 acc 0.4722222222222222\n",
      "epoch:  1963 loss:  1.2709735320504447 acc 0.4722222222222222\n",
      "epoch:  1964 loss:  1.271043235241546 acc 0.47333333333333333\n",
      "epoch:  1965 loss:  1.271112880679685 acc 0.47333333333333333\n",
      "epoch:  1966 loss:  1.271182467332333 acc 0.47444444444444445\n",
      "epoch:  1967 loss:  1.2712519941409406 acc 0.47555555555555556\n",
      "epoch:  1968 loss:  1.2713214600209202 acc 0.47555555555555556\n",
      "epoch:  1969 loss:  1.27139086386164 acc 0.47555555555555556\n",
      "epoch:  1970 loss:  1.2714602045264352 acc 0.47555555555555556\n",
      "epoch:  1971 loss:  1.2715294808526338 acc 0.47555555555555556\n",
      "epoch:  1972 loss:  1.2715986916515971 acc 0.47555555555555556\n",
      "epoch:  1973 loss:  1.271667835708778 acc 0.47555555555555556\n",
      "epoch:  1974 loss:  1.2717369117837907 acc 0.47555555555555556\n",
      "epoch:  1975 loss:  1.2718059186104993 acc 0.47555555555555556\n",
      "epoch:  1976 loss:  1.2718748548971208 acc 0.47555555555555556\n",
      "epoch:  1977 loss:  1.2719437193263412 acc 0.47555555555555556\n",
      "epoch:  1978 loss:  1.27201251055545 acc 0.47555555555555556\n",
      "epoch:  1979 loss:  1.2720812272164848 acc 0.47555555555555556\n",
      "epoch:  1980 loss:  1.2721498679163936 acc 0.47555555555555556\n",
      "epoch:  1981 loss:  1.27221843123721 acc 0.4766666666666667\n",
      "epoch:  1982 loss:  1.2722869157362398 acc 0.4766666666666667\n",
      "epoch:  1983 loss:  1.2723553199462614 acc 0.4766666666666667\n",
      "epoch:  1984 loss:  1.2724236423757407 acc 0.4766666666666667\n",
      "epoch:  1985 loss:  1.2724918815090511 acc 0.4766666666666667\n",
      "epoch:  1986 loss:  1.272560035806712 acc 0.4766666666666667\n",
      "epoch:  1987 loss:  1.2726281037056293 acc 0.4766666666666667\n",
      "epoch:  1988 loss:  1.2726960836193513 acc 0.4766666666666667\n",
      "epoch:  1989 loss:  1.272763973938329 acc 0.4766666666666667\n",
      "epoch:  1990 loss:  1.2728317730301848 acc 0.4766666666666667\n",
      "epoch:  1991 loss:  1.2728994792399875 acc 0.4766666666666667\n",
      "epoch:  1992 loss:  1.272967090890531 acc 0.4766666666666667\n",
      "epoch:  1993 loss:  1.2730346062826206 acc 0.4766666666666667\n",
      "epoch:  1994 loss:  1.2731020236953585 acc 0.47555555555555556\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:  1995 loss:  1.273169341386434 acc 0.47555555555555556\n",
      "epoch:  1996 loss:  1.2732365575924136 acc 0.47555555555555556\n",
      "epoch:  1997 loss:  1.273303670529031 acc 0.47555555555555556\n",
      "epoch:  1998 loss:  1.273370678391475 acc 0.47555555555555556\n",
      "epoch:  1999 loss:  1.2734375793546784 acc 0.47555555555555556\n",
      "epoch:  2000 loss:  1.2735043715735974 acc 0.47555555555555556\n",
      "0.24235839144139842\n"
     ]
    }
   ],
   "source": [
    "Experiment_num=1\n",
    "Experiment_result=np.zeros([categories+4,Experiment_num+2])#OA,AA,kappa,trn_time,tes_time\n",
    "epoch=2000\n",
    "lr=0.1\n",
    "scaler = MinMaxScaler(feature_range=(-1,1))\n",
    "X_EN=scaler.fit_transform(X_E)\n",
    "for i in range(Experiment_num):\n",
    "    rows_num, trn_num, tes_num, pre_num=generation_num(FLAG,labeled_data, \n",
    "                                                       rows_num, All_data)\n",
    "    X_trn=X_EN[trn_num,:]\n",
    "    y_trn=All_data[trn_num,-1]-1\n",
    "    \n",
    "    # training\n",
    "    para=init()\n",
    "    loss_trn=[]\n",
    "    for j in range(epoch) :\n",
    "        score,o,h,para,X=forward_propagation(X_trn,para)\n",
    "        loss=compute_loss(score,y_trn)\n",
    "        loss_trn.append(loss)\n",
    "        print('epoch: ',j+1, 'loss: ',loss, \n",
    "              'acc',np.mean(np.argmax(score,axis=1)==y_trn))\n",
    "        grad=back_propagation(score,y_trn,o,h,para,X)\n",
    "        para['W_ih']-=lr*grad['W_ih']\n",
    "        para['b_ih']-=lr*grad['b_ih']\n",
    "        para['W_ho']-=lr*grad['W_ho']\n",
    "        para['b_ho']-=lr*grad['b_ho']\n",
    "        if np.mean(np.argmax(score,axis=1)==y_trn)>0.9:\n",
    "            break\n",
    "    \n",
    "    X_tes=X_EN[tes_num,:]\n",
    "    tes_score,_,_,_,_=forward_propagation(X_tes,para)\n",
    "    y_pred=np.argmax(tes_score,axis=1)+1\n",
    "    y_tes=All_data[tes_num,-1]\n",
    "    print(np.mean(y_pred==y_tes))\n",
    "    \n",
    "#plt.plot(loss_trn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training sample 1342\n"
     ]
    }
   ],
   "source": [
    "#scaler = MinMaxScaler(feature_range=(-1,1))\n",
    "scaler=StandardScaler()\n",
    "X_EN=scaler.fit_transform(X_E)\n",
    "rows_num, trn_num, tes_num, pre_num=generation_num(FLAG,labeled_data, \n",
    "                                                       rows_num, All_data)\n",
    "X_trn=X_EN[trn_num,:]\n",
    "y_trn=All_data[trn_num,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C: [0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288]\n",
      "r: [3.0517578125e-05, 6.103515625e-05, 0.0001220703125, 0.000244140625, 0.00048828125, 0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16]\n"
     ]
    }
   ],
   "source": [
    "#chen 2016, Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks\n",
    "C=[2**i for i in range(-5,20)]\n",
    "R=[2**i for i in range(-15,5)]\n",
    "print('C:',C)\n",
    "print('r:',R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search CV cost 366.8892183303833 secs\n",
      "Best C is 64.000000, Best gamma is 0.125000\n"
     ]
    }
   ],
   "source": [
    "grid_SVM=GridSearchCV(SVC(decision_function_shape='ovo'), \n",
    "                      param_grid={'kernel':['rbf'],\n",
    "                        'C': C, 'gamma': R},cv=5)\n",
    "cv_time1=time.time()\n",
    "grid_SVM.fit(X_trn,y_trn)\n",
    "cv_time2=time.time()\n",
    "print('Grid search CV cost {} secs'.format(cv_time2-cv_time1))\n",
    "print(\"Best C is %f, Best gamma is %f\" %(grid_SVM.best_params_['C'],\n",
    "                                         grid_SVM.best_params_['gamma']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 0.0001220703125, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.301(+/-0.028) with {'gamma': 0.000244140625, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.304(+/-0.031) with {'gamma': 0.00048828125, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.307(+/-0.023) with {'gamma': 0.0009765625, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.320(+/-0.023) with {'gamma': 0.001953125, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.344(+/-0.013) with {'gamma': 0.00390625, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.365(+/-0.013) with {'gamma': 0.0078125, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.394(+/-0.012) with {'gamma': 0.015625, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.426(+/-0.013) with {'gamma': 0.03125, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.494(+/-0.015) with {'gamma': 0.0625, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.551(+/-0.042) with {'gamma': 0.125, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.579(+/-0.023) with {'gamma': 0.25, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.602(+/-0.020) with {'gamma': 0.5, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.621(+/-0.030) with {'gamma': 1, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.610(+/-0.043) with {'gamma': 2, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.563(+/-0.041) with {'gamma': 4, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.485(+/-0.048) with {'gamma': 8, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.398(+/-0.033) with {'gamma': 16, 'C': 0.03125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 0.0001220703125, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.301(+/-0.028) with {'gamma': 0.000244140625, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.304(+/-0.031) with {'gamma': 0.00048828125, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.307(+/-0.023) with {'gamma': 0.0009765625, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.320(+/-0.023) with {'gamma': 0.001953125, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.345(+/-0.014) with {'gamma': 0.00390625, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.367(+/-0.015) with {'gamma': 0.0078125, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.407(+/-0.014) with {'gamma': 0.015625, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.495(+/-0.018) with {'gamma': 0.03125, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.557(+/-0.027) with {'gamma': 0.0625, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.604(+/-0.038) with {'gamma': 0.125, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.627(+/-0.023) with {'gamma': 0.25, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.640(+/-0.027) with {'gamma': 0.5, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.627(+/-0.033) with {'gamma': 1, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.610(+/-0.044) with {'gamma': 2, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.568(+/-0.041) with {'gamma': 4, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.486(+/-0.048) with {'gamma': 8, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.398(+/-0.033) with {'gamma': 16, 'C': 0.0625, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 0.0001220703125, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.301(+/-0.028) with {'gamma': 0.000244140625, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.304(+/-0.031) with {'gamma': 0.00048828125, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.307(+/-0.023) with {'gamma': 0.0009765625, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.325(+/-0.022) with {'gamma': 0.001953125, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.347(+/-0.014) with {'gamma': 0.00390625, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.420(+/-0.017) with {'gamma': 0.0078125, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.498(+/-0.023) with {'gamma': 0.015625, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.550(+/-0.021) with {'gamma': 0.03125, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.609(+/-0.018) with {'gamma': 0.0625, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.646(+/-0.023) with {'gamma': 0.125, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.680(+/-0.024) with {'gamma': 0.25, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.709(+/-0.018) with {'gamma': 0.5, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.707(+/-0.025) with {'gamma': 1, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.654(+/-0.043) with {'gamma': 2, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.586(+/-0.035) with {'gamma': 4, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.499(+/-0.046) with {'gamma': 8, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.408(+/-0.037) with {'gamma': 16, 'C': 0.125, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 0.0001220703125, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.301(+/-0.028) with {'gamma': 0.000244140625, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.304(+/-0.031) with {'gamma': 0.00048828125, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.314(+/-0.026) with {'gamma': 0.0009765625, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.327(+/-0.022) with {'gamma': 0.001953125, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.416(+/-0.011) with {'gamma': 0.00390625, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.490(+/-0.021) with {'gamma': 0.0078125, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.567(+/-0.018) with {'gamma': 0.015625, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.615(+/-0.008) with {'gamma': 0.03125, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.648(+/-0.017) with {'gamma': 0.0625, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.694(+/-0.019) with {'gamma': 0.125, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.742(+/-0.012) with {'gamma': 0.25, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.779(+/-0.017) with {'gamma': 0.5, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.776(+/-0.016) with {'gamma': 1, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.755(+/-0.026) with {'gamma': 2, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.683(+/-0.033) with {'gamma': 4, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.536(+/-0.044) with {'gamma': 8, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.428(+/-0.035) with {'gamma': 16, 'C': 0.25, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 0.0001220703125, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.301(+/-0.028) with {'gamma': 0.000244140625, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.315(+/-0.032) with {'gamma': 0.00048828125, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.317(+/-0.024) with {'gamma': 0.0009765625, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.425(+/-0.019) with {'gamma': 0.001953125, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.499(+/-0.021) with {'gamma': 0.00390625, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.562(+/-0.017) with {'gamma': 0.0078125, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.624(+/-0.012) with {'gamma': 0.015625, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.651(+/-0.013) with {'gamma': 0.03125, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.694(+/-0.020) with {'gamma': 0.0625, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.752(+/-0.023) with {'gamma': 0.125, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.801(+/-0.012) with {'gamma': 0.25, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.815(+/-0.008) with {'gamma': 0.5, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.829(+/-0.012) with {'gamma': 1, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.820(+/-0.019) with {'gamma': 2, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.774(+/-0.018) with {'gamma': 4, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.677(+/-0.026) with {'gamma': 8, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.552(+/-0.029) with {'gamma': 16, 'C': 0.5, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 0.0001220703125, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.312(+/-0.031) with {'gamma': 0.000244140625, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.319(+/-0.031) with {'gamma': 0.00048828125, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.420(+/-0.025) with {'gamma': 0.0009765625, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.503(+/-0.018) with {'gamma': 0.001953125, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.554(+/-0.012) with {'gamma': 0.00390625, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.622(+/-0.020) with {'gamma': 0.0078125, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.657(+/-0.017) with {'gamma': 0.015625, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.697(+/-0.025) with {'gamma': 0.03125, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.753(+/-0.022) with {'gamma': 0.0625, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.808(+/-0.012) with {'gamma': 0.125, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.835(+/-0.013) with {'gamma': 0.25, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.012) with {'gamma': 0.5, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.864(+/-0.012) with {'gamma': 1, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.865(+/-0.005) with {'gamma': 2, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.835(+/-0.014) with {'gamma': 4, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.768(+/-0.010) with {'gamma': 8, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.680(+/-0.026) with {'gamma': 16, 'C': 1, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.026) with {'gamma': 6.103515625e-05, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.311(+/-0.029) with {'gamma': 0.0001220703125, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.318(+/-0.031) with {'gamma': 0.000244140625, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.415(+/-0.020) with {'gamma': 0.00048828125, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.503(+/-0.015) with {'gamma': 0.0009765625, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.550(+/-0.009) with {'gamma': 0.001953125, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.625(+/-0.018) with {'gamma': 0.00390625, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.659(+/-0.015) with {'gamma': 0.0078125, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.018) with {'gamma': 0.015625, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.741(+/-0.026) with {'gamma': 0.03125, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.809(+/-0.012) with {'gamma': 0.0625, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.843(+/-0.009) with {'gamma': 0.125, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.858(+/-0.006) with {'gamma': 0.25, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.877(+/-0.010) with {'gamma': 0.5, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.013) with {'gamma': 1, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.884(+/-0.011) with {'gamma': 2, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.017) with {'gamma': 4, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.776(+/-0.007) with {'gamma': 8, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 2, 'kernel': 'rbf'}\n",
      "Mean test error: 0.300(+/-0.027) with {'gamma': 3.0517578125e-05, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.311(+/-0.029) with {'gamma': 6.103515625e-05, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.318(+/-0.029) with {'gamma': 0.0001220703125, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.411(+/-0.021) with {'gamma': 0.000244140625, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.504(+/-0.021) with {'gamma': 0.00048828125, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.553(+/-0.008) with {'gamma': 0.0009765625, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.621(+/-0.019) with {'gamma': 0.001953125, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.656(+/-0.013) with {'gamma': 0.00390625, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.018) with {'gamma': 0.0078125, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.744(+/-0.017) with {'gamma': 0.015625, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.800(+/-0.014) with {'gamma': 0.03125, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.841(+/-0.015) with {'gamma': 0.0625, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.869(+/-0.010) with {'gamma': 0.125, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.888(+/-0.010) with {'gamma': 0.25, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.010) with {'gamma': 0.5, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.017) with {'gamma': 1, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.884(+/-0.012) with {'gamma': 2, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.847(+/-0.013) with {'gamma': 4, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.776(+/-0.009) with {'gamma': 8, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 4, 'kernel': 'rbf'}\n",
      "Mean test error: 0.311(+/-0.030) with {'gamma': 3.0517578125e-05, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.319(+/-0.029) with {'gamma': 6.103515625e-05, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.409(+/-0.024) with {'gamma': 0.0001220703125, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.501(+/-0.020) with {'gamma': 0.000244140625, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.550(+/-0.003) with {'gamma': 0.00048828125, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.620(+/-0.019) with {'gamma': 0.0009765625, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.659(+/-0.015) with {'gamma': 0.001953125, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.689(+/-0.018) with {'gamma': 0.00390625, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.734(+/-0.018) with {'gamma': 0.0078125, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.796(+/-0.012) with {'gamma': 0.015625, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.832(+/-0.014) with {'gamma': 0.03125, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.874(+/-0.012) with {'gamma': 0.0625, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.009) with {'gamma': 0.125, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.012) with {'gamma': 0.25, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.906(+/-0.011) with {'gamma': 0.5, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.015) with {'gamma': 1, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.886(+/-0.013) with {'gamma': 2, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.014) with {'gamma': 4, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 8, 'kernel': 'rbf'}\n",
      "Mean test error: 0.319(+/-0.030) with {'gamma': 3.0517578125e-05, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.408(+/-0.025) with {'gamma': 6.103515625e-05, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.496(+/-0.018) with {'gamma': 0.0001220703125, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.550(+/-0.008) with {'gamma': 0.000244140625, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.617(+/-0.019) with {'gamma': 0.00048828125, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.662(+/-0.014) with {'gamma': 0.0009765625, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.688(+/-0.018) with {'gamma': 0.001953125, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.730(+/-0.014) with {'gamma': 0.00390625, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.791(+/-0.009) with {'gamma': 0.0078125, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.829(+/-0.017) with {'gamma': 0.015625, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.872(+/-0.010) with {'gamma': 0.03125, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.892(+/-0.012) with {'gamma': 0.0625, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.911(+/-0.016) with {'gamma': 0.125, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.911(+/-0.014) with {'gamma': 0.25, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.013) with {'gamma': 0.5, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.019) with {'gamma': 1, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.887(+/-0.012) with {'gamma': 2, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 16, 'kernel': 'rbf'}\n",
      "Mean test error: 0.408(+/-0.025) with {'gamma': 3.0517578125e-05, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.494(+/-0.017) with {'gamma': 6.103515625e-05, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.551(+/-0.015) with {'gamma': 0.0001220703125, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.618(+/-0.018) with {'gamma': 0.000244140625, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.664(+/-0.021) with {'gamma': 0.00048828125, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.686(+/-0.012) with {'gamma': 0.0009765625, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.726(+/-0.014) with {'gamma': 0.001953125, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.794(+/-0.010) with {'gamma': 0.00390625, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.823(+/-0.017) with {'gamma': 0.0078125, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.861(+/-0.021) with {'gamma': 0.015625, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.015) with {'gamma': 0.03125, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.016) with {'gamma': 0.0625, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.914(+/-0.014) with {'gamma': 0.125, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.911(+/-0.013) with {'gamma': 0.25, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.016) with {'gamma': 0.5, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.906(+/-0.018) with {'gamma': 1, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.884(+/-0.011) with {'gamma': 2, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 32, 'kernel': 'rbf'}\n",
      "Mean test error: 0.495(+/-0.018) with {'gamma': 3.0517578125e-05, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.551(+/-0.016) with {'gamma': 6.103515625e-05, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.622(+/-0.020) with {'gamma': 0.0001220703125, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.669(+/-0.019) with {'gamma': 0.000244140625, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.682(+/-0.008) with {'gamma': 0.00048828125, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.732(+/-0.017) with {'gamma': 0.0009765625, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.788(+/-0.010) with {'gamma': 0.001953125, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.817(+/-0.018) with {'gamma': 0.00390625, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.854(+/-0.020) with {'gamma': 0.0078125, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.885(+/-0.014) with {'gamma': 0.015625, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.015) with {'gamma': 0.03125, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.914(+/-0.016) with {'gamma': 0.0625, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.925(+/-0.015) with {'gamma': 0.125, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.015) with {'gamma': 0.25, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.015) with {'gamma': 0.5, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.016) with {'gamma': 1, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 64, 'kernel': 'rbf'}\n",
      "Mean test error: 0.551(+/-0.016) with {'gamma': 3.0517578125e-05, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.621(+/-0.019) with {'gamma': 6.103515625e-05, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.669(+/-0.017) with {'gamma': 0.0001220703125, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.687(+/-0.007) with {'gamma': 0.000244140625, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.732(+/-0.015) with {'gamma': 0.00048828125, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.783(+/-0.011) with {'gamma': 0.0009765625, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.815(+/-0.015) with {'gamma': 0.001953125, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.844(+/-0.024) with {'gamma': 0.00390625, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.879(+/-0.017) with {'gamma': 0.0078125, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.016) with {'gamma': 0.015625, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.917(+/-0.015) with {'gamma': 0.03125, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.918(+/-0.012) with {'gamma': 0.0625, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.923(+/-0.016) with {'gamma': 0.125, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.015) with {'gamma': 0.25, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.015) with {'gamma': 0.5, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.898(+/-0.019) with {'gamma': 1, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 128, 'kernel': 'rbf'}\n",
      "Mean test error: 0.624(+/-0.021) with {'gamma': 3.0517578125e-05, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.667(+/-0.017) with {'gamma': 6.103515625e-05, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.691(+/-0.011) with {'gamma': 0.0001220703125, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.729(+/-0.015) with {'gamma': 0.000244140625, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.784(+/-0.008) with {'gamma': 0.00048828125, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.811(+/-0.012) with {'gamma': 0.0009765625, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.837(+/-0.023) with {'gamma': 0.001953125, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.869(+/-0.014) with {'gamma': 0.00390625, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.016) with {'gamma': 0.0078125, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.911(+/-0.013) with {'gamma': 0.015625, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.921(+/-0.010) with {'gamma': 0.03125, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.918(+/-0.017) with {'gamma': 0.0625, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.919(+/-0.016) with {'gamma': 0.125, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.904(+/-0.018) with {'gamma': 0.25, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.015) with {'gamma': 0.5, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.019) with {'gamma': 1, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 256, 'kernel': 'rbf'}\n",
      "Mean test error: 0.668(+/-0.016) with {'gamma': 3.0517578125e-05, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.010) with {'gamma': 6.103515625e-05, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.732(+/-0.017) with {'gamma': 0.0001220703125, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.784(+/-0.009) with {'gamma': 0.000244140625, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.807(+/-0.012) with {'gamma': 0.00048828125, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.832(+/-0.021) with {'gamma': 0.0009765625, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.863(+/-0.016) with {'gamma': 0.001953125, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.883(+/-0.015) with {'gamma': 0.00390625, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.015) with {'gamma': 0.0078125, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.920(+/-0.014) with {'gamma': 0.015625, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.920(+/-0.006) with {'gamma': 0.03125, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.915(+/-0.013) with {'gamma': 0.0625, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.913(+/-0.014) with {'gamma': 0.125, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.013) with {'gamma': 0.25, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.015) with {'gamma': 0.5, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.019) with {'gamma': 1, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 512, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.011) with {'gamma': 3.0517578125e-05, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.732(+/-0.014) with {'gamma': 6.103515625e-05, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.780(+/-0.010) with {'gamma': 0.0001220703125, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.804(+/-0.017) with {'gamma': 0.000244140625, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.824(+/-0.019) with {'gamma': 0.00048828125, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.855(+/-0.021) with {'gamma': 0.0009765625, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.879(+/-0.016) with {'gamma': 0.001953125, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.015) with {'gamma': 0.00390625, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.906(+/-0.012) with {'gamma': 0.0078125, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.920(+/-0.011) with {'gamma': 0.015625, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.917(+/-0.010) with {'gamma': 0.03125, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.914(+/-0.014) with {'gamma': 0.0625, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.908(+/-0.017) with {'gamma': 0.125, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.901(+/-0.012) with {'gamma': 0.25, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.015) with {'gamma': 0.5, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 1024, 'kernel': 'rbf'}\n",
      "Mean test error: 0.729(+/-0.016) with {'gamma': 3.0517578125e-05, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.777(+/-0.011) with {'gamma': 6.103515625e-05, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.800(+/-0.015) with {'gamma': 0.0001220703125, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.824(+/-0.017) with {'gamma': 0.000244140625, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.851(+/-0.022) with {'gamma': 0.00048828125, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.875(+/-0.022) with {'gamma': 0.0009765625, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.016) with {'gamma': 0.001953125, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.012) with {'gamma': 0.00390625, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.914(+/-0.012) with {'gamma': 0.0078125, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.915(+/-0.010) with {'gamma': 0.015625, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.914(+/-0.015) with {'gamma': 0.03125, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.909(+/-0.009) with {'gamma': 0.0625, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.017) with {'gamma': 0.125, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.012) with {'gamma': 0.25, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 2048, 'kernel': 'rbf'}\n",
      "Mean test error: 0.776(+/-0.009) with {'gamma': 3.0517578125e-05, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.800(+/-0.017) with {'gamma': 6.103515625e-05, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.823(+/-0.019) with {'gamma': 0.0001220703125, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.018) with {'gamma': 0.000244140625, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.873(+/-0.023) with {'gamma': 0.00048828125, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.889(+/-0.022) with {'gamma': 0.0009765625, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.001953125, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.013) with {'gamma': 0.00390625, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.917(+/-0.013) with {'gamma': 0.0078125, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.911(+/-0.011) with {'gamma': 0.015625, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.913(+/-0.018) with {'gamma': 0.03125, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.013) with {'gamma': 0.0625, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.016) with {'gamma': 0.125, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.014) with {'gamma': 0.25, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 4096, 'kernel': 'rbf'}\n",
      "Mean test error: 0.797(+/-0.018) with {'gamma': 3.0517578125e-05, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.822(+/-0.023) with {'gamma': 6.103515625e-05, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.844(+/-0.019) with {'gamma': 0.0001220703125, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.868(+/-0.019) with {'gamma': 0.000244140625, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.884(+/-0.021) with {'gamma': 0.00048828125, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.021) with {'gamma': 0.0009765625, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.015) with {'gamma': 0.001953125, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.906(+/-0.014) with {'gamma': 0.00390625, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.908(+/-0.015) with {'gamma': 0.0078125, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.907(+/-0.015) with {'gamma': 0.015625, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.910(+/-0.020) with {'gamma': 0.03125, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.011) with {'gamma': 0.0625, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.016) with {'gamma': 0.125, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 8192, 'kernel': 'rbf'}\n",
      "Mean test error: 0.816(+/-0.024) with {'gamma': 3.0517578125e-05, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.841(+/-0.022) with {'gamma': 6.103515625e-05, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.863(+/-0.019) with {'gamma': 0.0001220703125, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.885(+/-0.021) with {'gamma': 0.000244140625, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.024) with {'gamma': 0.00048828125, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.901(+/-0.009) with {'gamma': 0.0009765625, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.908(+/-0.013) with {'gamma': 0.001953125, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.909(+/-0.011) with {'gamma': 0.00390625, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.012) with {'gamma': 0.0078125, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.013) with {'gamma': 0.015625, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.904(+/-0.020) with {'gamma': 0.03125, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.897(+/-0.011) with {'gamma': 0.0625, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.016) with {'gamma': 0.125, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 16384, 'kernel': 'rbf'}\n",
      "Mean test error: 0.837(+/-0.023) with {'gamma': 3.0517578125e-05, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.862(+/-0.020) with {'gamma': 6.103515625e-05, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.880(+/-0.024) with {'gamma': 0.0001220703125, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.020) with {'gamma': 0.000244140625, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.010) with {'gamma': 0.00048828125, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.010) with {'gamma': 0.0009765625, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.901(+/-0.010) with {'gamma': 0.001953125, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.011) with {'gamma': 0.00390625, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.012) with {'gamma': 0.0078125, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.011) with {'gamma': 0.015625, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.015) with {'gamma': 0.03125, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.010) with {'gamma': 0.0625, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.016) with {'gamma': 0.125, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 32768, 'kernel': 'rbf'}\n",
      "Mean test error: 0.862(+/-0.020) with {'gamma': 3.0517578125e-05, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.881(+/-0.020) with {'gamma': 6.103515625e-05, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.019) with {'gamma': 0.0001220703125, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.892(+/-0.019) with {'gamma': 0.000244140625, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.009) with {'gamma': 0.00048828125, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.010) with {'gamma': 0.0009765625, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.008) with {'gamma': 0.001953125, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.011) with {'gamma': 0.00390625, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.901(+/-0.011) with {'gamma': 0.0078125, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.013) with {'gamma': 0.015625, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.015) with {'gamma': 0.03125, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.012) with {'gamma': 0.0625, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.016) with {'gamma': 0.125, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 65536, 'kernel': 'rbf'}\n",
      "Mean test error: 0.879(+/-0.022) with {'gamma': 3.0517578125e-05, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.020) with {'gamma': 6.103515625e-05, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.013) with {'gamma': 0.0001220703125, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.010) with {'gamma': 0.000244140625, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.903(+/-0.011) with {'gamma': 0.00048828125, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.013) with {'gamma': 0.0009765625, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.008) with {'gamma': 0.001953125, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.905(+/-0.008) with {'gamma': 0.00390625, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.012) with {'gamma': 0.0078125, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.891(+/-0.013) with {'gamma': 0.015625, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.016) with {'gamma': 0.03125, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.011) with {'gamma': 0.0625, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.016) with {'gamma': 0.125, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 131072, 'kernel': 'rbf'}\n",
      "Mean test error: 0.888(+/-0.023) with {'gamma': 3.0517578125e-05, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.017) with {'gamma': 6.103515625e-05, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.011) with {'gamma': 0.0001220703125, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.902(+/-0.014) with {'gamma': 0.000244140625, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.009) with {'gamma': 0.00048828125, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.012) with {'gamma': 0.0009765625, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.898(+/-0.011) with {'gamma': 0.001953125, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.010) with {'gamma': 0.00390625, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.012) with {'gamma': 0.0078125, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.891(+/-0.011) with {'gamma': 0.015625, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.897(+/-0.017) with {'gamma': 0.03125, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.011) with {'gamma': 0.0625, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.016) with {'gamma': 0.125, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 262144, 'kernel': 'rbf'}\n",
      "Mean test error: 0.888(+/-0.016) with {'gamma': 3.0517578125e-05, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.890(+/-0.010) with {'gamma': 6.103515625e-05, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.893(+/-0.014) with {'gamma': 0.0001220703125, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.016) with {'gamma': 0.000244140625, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.898(+/-0.018) with {'gamma': 0.00048828125, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.901(+/-0.012) with {'gamma': 0.0009765625, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.900(+/-0.008) with {'gamma': 0.001953125, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.009) with {'gamma': 0.00390625, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.887(+/-0.008) with {'gamma': 0.0078125, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.889(+/-0.011) with {'gamma': 0.015625, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.013) with {'gamma': 0.03125, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.011) with {'gamma': 0.0625, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.899(+/-0.016) with {'gamma': 0.125, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.895(+/-0.014) with {'gamma': 0.25, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.896(+/-0.014) with {'gamma': 0.5, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.894(+/-0.019) with {'gamma': 1, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.882(+/-0.011) with {'gamma': 2, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.846(+/-0.015) with {'gamma': 4, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.775(+/-0.009) with {'gamma': 8, 'C': 524288, 'kernel': 'rbf'}\n",
      "Mean test error: 0.690(+/-0.024) with {'gamma': 16, 'C': 524288, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "for mu_SVM,std_SVM,param_SVM in zip(grid_SVM.cv_results_['mean_test_score'],\n",
    "                                   grid_SVM.cv_results_['std_test_score'],\n",
    "                                   grid_SVM.cv_results_['params']):\n",
    "    print('Mean test error: %.3f(+/-%.3f) with %r'%(mu_SVM,std_SVM,param_SVM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第0次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第1次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第2次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第3次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第4次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第5次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第6次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第7次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第8次实验，模型评估阶段完成！！!\n",
      "number of training sample 1342\n",
      "Random number generated Finished!\n",
      "第9次实验，模型评估阶段完成！！!\n"
     ]
    }
   ],
   "source": [
    "Experiment_result=np.zeros([categories+4,12])#OA,AA,kappa,trn_time,tes_time\n",
    "kappa=0\n",
    "best_trn_num=[]\n",
    "best_pre_num=[]\n",
    "best_tes_num=[]\n",
    "for count in range(10):\n",
    "    rows_num,trn_num,tes_num,pre_num=generation_num(FLAG,labeled_data,rows_num,All_data)\n",
    "    print('Random number generated Finished!')\n",
    "    X_trn=X_EN[trn_num,:]\n",
    "    X_tes=X_EN[tes_num,:]\n",
    "    y_trn=All_data[trn_num,-1]\n",
    "    y_tes=All_data[tes_num,-1]\n",
    "    \n",
    "    SVM_model=SVC(C=grid_SVM.best_params_['C'],kernel='rbf',gamma=grid_SVM.best_params_['gamma']\n",
    "                  ,decision_function_shape='ovo')\n",
    "    trn_time1=time.time()\n",
    "    SVM_model.fit(X_trn,y_trn)\n",
    "    trn_time2=time.time()\n",
    "    \n",
    "    tes_time1=time.time()\n",
    "    y_pred_SVM=SVM_model.predict(X_tes)\n",
    "    tes_time2=time.time()\n",
    "    \n",
    "    if cohen_kappa_score(y_tes,y_pred_SVM)>=kappa:\n",
    "        best_model=SVM_model\n",
    "        best_trn_num=trn_num\n",
    "        best_pre_num=pre_num\n",
    "        best_tes_num=tes_num\n",
    "        kappa=cohen_kappa_score(y_tes,y_pred_SVM)\n",
    "    \n",
    "    num_tes=np.zeros([categories-1])\n",
    "    num_tes_pred=np.zeros([categories-1])\n",
    "    for k in y_tes:\n",
    "        num_tes[k-1]=num_tes[k-1]+1\n",
    "    for j in range(y_tes.shape[0]):\n",
    "        if y_tes[j]==y_pred_SVM[j]:\n",
    "            num_tes_pred[y_tes[j]-1]=num_tes_pred[y_tes[j]-1]+1\n",
    "\n",
    "    Acc=num_tes_pred/num_tes*100\n",
    "\n",
    "    Experiment_result[0,count]=np.mean(y_tes==y_pred_SVM)*100#OA\n",
    "    Experiment_result[1,count]=np.mean(Acc)#AA\n",
    "    Experiment_result[2,count]=cohen_kappa_score(y_tes,y_pred_SVM)*100#Kappa\n",
    "    Experiment_result[3, count] = trn_time2 - trn_time1\n",
    "    Experiment_result[4, count] = tes_time2 - tes_time1\n",
    "    Experiment_result[5:,count]=Acc\n",
    "\n",
    "    print('第{}次实验，模型评估阶段完成！！!'.format(count))\n",
    "    \n",
    "Experiment_result[:,-2]=np.mean(Experiment_result[:,0:-2],axis=1)\n",
    "Experiment_result[:,-1]=np.std(Experiment_result[:,0:-2],axis=1)\n",
    "scio.savemat('EMP_svm_result_'+str(FLAG)+'.mat',{'data':Experiment_result})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8667340294150668\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(y_tes==y_pred_SVM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_pre=X_EN[best_pre_num,:]\n",
    "y_pre=All_data[best_pre_num,-1]\n",
    "y_pre_SVM=best_model.predict(X_pre)\n",
    "y_trn=All_data[best_trn_num,-1]\n",
    "y_tes=All_data[best_tes_num,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text yticklabel objects>)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_Result=np.zeros([All_data.shape[0]])\n",
    "y_Result[best_trn_num]=y_trn\n",
    "\n",
    "y_Result_SVM=y_Result.copy()\n",
    "y_Result_SVM[best_pre_num]=y_pre_SVM\n",
    "\n",
    "y_Result_GT=y_Result.copy()\n",
    "y_Result_GT[best_tes_num]=y_tes\n",
    "\n",
    "plt.subplots(figsize=[10,10])\n",
    "plt.subplot(1,2,1)\n",
    "a1=plt.imshow(y_Result_GT.reshape(r,c),cmap='jet')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.subplot(1,2,2)\n",
    "a2=plt.imshow(y_Result_SVM.reshape(r,c),cmap='jet')\n",
    "plt.xticks([])\n",
    "plt.yticks([])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python py35matlab17b",
   "language": "python",
   "name": "py4matlab"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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